Diagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein-based machine learning.

Truong Phan-Xuan Nguyen, Minh-Khang Le, Sittiruk Roytrakul, Shanop Shuangshoti, Nakarin Kitkumthorn, Somboon Keelawat
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Abstract

Background: Although the criteria for follicular-pattern thyroid tumors are well-established, diagnosing these lesions remains challenging in some cases. In the recent World Health Organization Classification of Endocrine and Neuroendocrine Tumors (5th edition), the invasive encapsulated follicular variant of papillary thyroid carcinoma was reclassified as its own entity. It is crucial to differentiate this variant of papillary thyroid carcinoma from low-risk follicular pattern tumors due to their shared morphological characteristics. Proteomics holds significant promise for detecting and quantifying protein biomarkers. We investigated the potential value of a protein biomarker panel defined by machine learning for identifying the invasive encapsulated follicular variant of papillary thyroid carcinoma, initially using formalin- fixed paraffin-embedded samples.

Methods: We developed a supervised machine-learning model and tested its performance using proteomics data from 46 thyroid tissue samples.

Results: We applied a random forest classifier utilizing five protein biomarkers (ZEB1, NUP98, C2C2L, NPAP1, and KCNJ3). This classifier achieved areas under the curve (AUCs) of 1.00 and accuracy rates of 1.00 in training samples for distinguishing the invasive encapsulated follicular variant of papillary thyroid carcinoma from non-malignant samples. Additionally, we analyzed the performance of single-protein/gene receiver operating characteristic in differentiating the invasive encapsulated follicular variant of papillary thyroid carcinoma from others within The Cancer Genome Atlas projects, which yielded an AUC > 0.5.

Conclusions: We demonstrated that integration of high-throughput proteomics with machine learning can effectively differentiate the invasive encapsulated follicular variant of papillary thyroid carcinoma from other follicular pattern thyroid tumors.

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通过基于蛋白质的机器学习诊断浸润性包膜滤泡变异型甲状腺乳头状癌
背景:尽管滤泡型甲状腺肿瘤的标准已经确立,但在某些病例中诊断这些病变仍然具有挑战性。在最近的世界卫生组织内分泌和神经内分泌肿瘤分类(第五版)中,甲状腺乳头状癌的浸润性包膜滤泡变异型被重新归类为一个独立的实体。由于这种甲状腺乳头状癌与低危滤泡型肿瘤具有共同的形态学特征,因此将其与低危滤泡型肿瘤区分开来至关重要。蛋白质组学在检测和量化蛋白质生物标志物方面大有可为。我们首先使用福尔马林固定石蜡包埋样本,研究了通过机器学习定义的蛋白质生物标记物面板在鉴别甲状腺乳头状癌浸润性包膜滤泡变异型方面的潜在价值:我们开发了一个有监督的机器学习模型,并使用来自 46 个甲状腺组织样本的蛋白质组学数据测试了该模型的性能:我们利用五个蛋白质生物标记物(ZEB1、NUP98、C2C2L、NPAP1和KCNJ3)建立了随机森林分类器。该分类器的曲线下面积(AUC)达到了1.00,在训练样本中区分甲状腺乳头状癌浸润性包膜滤泡变异型与非恶性样本的准确率也达到了1.00。此外,我们还分析了单个蛋白/基因接收操作特征在区分甲状腺乳头状癌浸润性包裹性滤泡变异型与癌症基因组图谱项目中其他变异型方面的性能,其AUC > 0.5:我们证明了高通量蛋白质组学与机器学习的整合能有效区分浸润性包膜滤泡型甲状腺乳头状癌和其他滤泡型甲状腺肿瘤。
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来源期刊
CiteScore
5.00
自引率
4.20%
发文量
45
审稿时长
14 weeks
期刊介绍: The Journal of Pathology and Translational Medicine is an open venue for the rapid publication of major achievements in various fields of pathology, cytopathology, and biomedical and translational research. The Journal aims to share new insights into the molecular and cellular mechanisms of human diseases and to report major advances in both experimental and clinical medicine, with a particular emphasis on translational research. The investigations of human cells and tissues using high-dimensional biology techniques such as genomics and proteomics will be given a high priority. Articles on stem cell biology are also welcome. The categories of manuscript include original articles, review and perspective articles, case studies, brief case reports, and letters to the editor.
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